School of Life Sciences Research, College of Life Sciences, University of Dundee, Dundee, UK.
Proteins. 2011 Apr;79(4):1027-33. doi: 10.1002/prot.22914. Epub 2011 Jan 18.
Production of diffracting crystals is a critical step in determining the three-dimensional structure of a protein by X-ray crystallography. Computational techniques to rank proteins by their propensity to yield diffraction-quality crystals can improve efficiency in obtaining structural data by guiding both protein selection and construct design. XANNpred comprises a pair of artificial neural networks that each predict the propensity of a selected protein sequence to produce diffraction-quality crystals by current structural biology techniques. Blind tests show XANNpred has accuracy and Matthews correlation values ranging from 75% to 81% and 0.50 to 0.63 respectively; values of area under the receiver operator characteristic (ROC) curve range from 0.81 to 0.88. On blind test data XANNpred outperforms the other available algorithms XtalPred, PXS, OB-Score, and ParCrys. XANNpred also guides construct design by presenting graphs of predicted propensity for diffraction-quality crystals against residue sequence position. The XANNpred-SG algorithm is likely to be most useful to target selection in structural genomics consortia, while the XANNpred-PDB algorithm is more suited to the general structural biology community. XANNpred predictions that include sliding window graphs are freely available from http://www.compbio.dundee.ac.uk/xannpred
衍射晶体的生产是通过 X 射线晶体学确定蛋白质三维结构的关键步骤。通过计算技术对蛋白质进行排序，预测其产生具有衍射质量晶体的倾向，可以通过指导蛋白质选择和结构设计来提高获得结构数据的效率。XANNpred 由一对人工神经网络组成，它们分别预测所选蛋白质序列通过当前结构生物学技术产生衍射质量晶体的倾向。盲测结果表明，XANNpred 的准确率和 Matthews 相关系数分别在 75%到 81%和 0.50 到 0.63 之间；接收器操作特性（ROC）曲线下的面积值范围在 0.81 到 0.88 之间。在盲测数据中，XANNpred 的性能优于其他可用的算法 XtalPred、PXS、OB-Score 和 ParCrys。XANNpred 还通过显示预测对衍射质量晶体的倾向与残基序列位置的关系图来指导结构设计。XANNpred-SG 算法可能对结构基因组学联合体的目标选择最有用，而 XANNpred-PDB 算法更适合一般结构生物学界。包含滑动窗口图的 XANNpred 预测可从 http://www.compbio.dundee.ac.uk/xannpred 免费获得。
